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Cell death is an essential process where the body gets rid of old or damaged cells. Cell proliferation and death need to be balanced, as an imbalance between the two may lead to cancer or autoimmune diseases.
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Christian de Duve discovered “autophagy,” a process in which cellular components are engulfed by membrane-bound organelles called autophagosomes. The autophagosomes then fuse with lysosomes to digest the enclosed contents. Autophagy is generally activated in cells to prevent cell death. However, cell death is triggered when the damage is beyond repair.
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相关实验视频

Updated: Jan 17, 2026

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在细胞死亡研究中的信息学和数据科学.

Hue Vu-Thi1, Huy Than Quang2, Vu-Hung Nguyen2

  • 1The Interdisciplinary Research Group on Biomedicine and Health, International School, Vietnam National University, Hanoi, Vietnam; Faculty of Applied Sciences, International School, Vietnam National University, Hanoi, Vietnam.

Progress in molecular biology and translational science
|September 22, 2025
PubMed
概括

信息学和数据科学通过改进数据分析和诊断来增强细胞死亡研究. 这些先进的方法解决了量化和定义细胞死亡的挑战,这对于理解癌症等疾病至关重要.

关键词:
细胞灭亡 (apoptosis) 是一种死亡的过程.细胞死亡 细胞死亡数据科学是数据科学.信息学是一种信息学.

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科学领域:

  • 细胞生物学 细胞生物学
  • 生物信息学是一种生物信息学.
  • 计算生物学是一种计算生物学.

背景情况:

  • 细胞死亡对于组织平衡和去除受损细胞至关重要.
  • 细胞死亡途径的失调与癌症和自身免疫性疾病等疾病有关.
  • 传统的细胞死亡研究在量化,分类和检测方面面临着挑战.

研究的目的:

  • 在细胞死亡研究中提供信息学和数据科学应用的概述.
  • 讨论传统细胞死亡研究方法的优点和局限性.
  • 探索信息学,数据科学和人工智能 (AI) 如何克服现有挑战.

主要方法:

  • 对细胞死亡研究中的信息学和数据科学现有文献的审查.
  • 对传统细胞死亡研究方法的分析.
  • 讨论人工智能驱动的细胞死亡分析方法.

主要成果:

  • 在细胞死亡研究中,信息学和数据科学比传统方法具有显著的优势.
  • 这些计算方法提高了复杂数据处理和生物系统建模的效率.
  • 这些方法的实施提高了诊断程序的性能.

结论:

  • 信息学和数据科学正在彻底改变细胞死亡研究.
  • 人工智能和数据驱动的策略提供了强大的工具来克服传统方法的局限性.
  • 这种整合有助于制定更有效的研究计划,提高细胞死亡相关疾病的诊断能力.